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Gondoputro W, Doan P, Katelaris A, Scheltema MJ, Geboers B, Agrawal S, Liu Z, Yaxley J, Savdie R, Rasiah K, Frydenberg M, Roberts MJ, Malouf D, Wong D, Shnier R, Delprado W, Emmett L, Stricker PD, Thompson J. 68Ga-PSMA-PET/CT in addition to mpMRI in men undergoing biopsy during active surveillance for low- to intermediate-risk prostate cancer: study protocol for a prospective cross-sectional study. Transl Androl Urol 2023; 12:1598-1606. [PMID: 37969779 PMCID: PMC10643393 DOI: 10.21037/tau-22-708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 08/13/2023] [Indexed: 11/17/2023] Open
Abstract
Background In active surveillance there is significant interest in whether imaging modalities such as multiparametric magnetic resonance imaging (mpMRI) or 68Gallium prostate-specific membrane antigen positron emission tomography/computerized tomography (68Ga-PSMA-PET/CT) can improve the detection of progression to clinically significant prostate cancer (csPCa) and thus reduce the frequency of prostate biopsies and associated morbidity. Recent studies have demonstrated the value of mpMRI in active surveillance; however, mpMRI does miss a proportion of disease progression and thus alone cannot replace biopsy. To date, prostate-specific membrane antigen positron emission tomography (PSMA-PET) has shown additive value to mpMRI in its ability to detect prostate cancer (PCa) in the primary diagnostic setting. Our objective is to evaluate the diagnostic utility of PSMA-PET to detect progression to csPCa in active surveillance patients. Methods We will perform a prospective, cross-sectional, partially blinded, multicentre clinical trial evaluating the additive value of PSMA-PET with mpMRI against saturation transperineal template prostate biopsy. Two hundred and twenty-five men will be recruited who have newly diagnosed PCa which is suitable for active surveillance. Following enrolment, patients will undergo a PSMA-PET and mpMRI within 3 months of a repeat 12-month confirmatory biopsy. Patients who remain on active surveillance after confirmatory biopsy will then be planned to have a further mpMRI and PSMA-PET prior to a repeat biopsy in 3-4 years. The primary outcome is to assess the ability of PSMA-PET to detect or exclude significant malignancy on repeat biopsy. Secondary outcomes include (I) assess the comparative diagnostic accuracies of mpMRI and PSMA-PET alone [sensitivity/specificity/negative predictive value (NPV)/positive predictive value (PPV)] to detect progression on biopsy based on predefined histologic criteria for progression; (II) comparison of index lesion identification by template biopsies vs. MRI targeted lesions vs. PSMA targeted lesions; (III) evaluation of concordance of lesions identified on final histopathology and each imaging modality (PSMA-PET and/or mpMRI) in the subset of patients proceeding to RP. Discussion The results of this trial will define the role of PSMA-PET in active surveillance and potentially reduce the number of biopsies needed to detect progression to csPCa. Trial Registration The current trial was registered with the ANZCTR on the 3/2/2022 with the trial ID ACTRN12622000188730, it is accessible at https://www.anzctr.org.au/.
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Affiliation(s)
- William Gondoputro
- St Vincent’s Prostate Cancer Research Centre, Sydney, Australia
- Garvan Institute of Medical Research, Sydney, Australia
| | - Paul Doan
- St Vincent’s Prostate Cancer Research Centre, Sydney, Australia
- Garvan Institute of Medical Research, Sydney, Australia
| | - Athos Katelaris
- St Vincent’s Prostate Cancer Research Centre, Sydney, Australia
- Garvan Institute of Medical Research, Sydney, Australia
| | - Matthijs J. Scheltema
- St Vincent’s Prostate Cancer Research Centre, Sydney, Australia
- Garvan Institute of Medical Research, Sydney, Australia
| | - Bart Geboers
- St Vincent’s Prostate Cancer Research Centre, Sydney, Australia
- Garvan Institute of Medical Research, Sydney, Australia
| | - Shikha Agrawal
- St Vincent’s Prostate Cancer Research Centre, Sydney, Australia
- Department of Urology, St Vincent’s Private Hospital Sydney, Sydney, Australia
| | - Zhixin Liu
- St Vincent’s Prostate Cancer Research Centre, Sydney, Australia
- Garvan Institute of Medical Research, Sydney, Australia
| | - John Yaxley
- Department of Urology, Wesley Urology Clinic, Brisbane, Australia
| | - Richard Savdie
- Department of Urology, St Vincent’s Private Hospital Sydney, Sydney, Australia
- Department of Urology, Prince of Wales Hospital, Sydney, Australia
| | - Kris Rasiah
- Department of Urology, Royal North Shore Hospital, Sydney, Australia
| | - Mark Frydenberg
- Department of Urology, Cabrini Hospital Malvern, Melbourne, Australia
| | - Matthew J. Roberts
- Department of Urology, Royal Brisbane and Women’s Hospital, Brisbane, Australia
| | - David Malouf
- Department of Urology, St George Hospital, Sydney, Australia
| | - David Wong
- I-MED Radiology Network, Sydney, Australia
| | - Ron Shnier
- I-MED Radiology Network, Sydney, Australia
| | | | - Louise Emmett
- Garvan Institute of Medical Research, Sydney, Australia
- Department of Theranostics and Nuclear Medicine, St Vincent’s Hospital Sydney, Sydney, Australia
| | - Phillip D. Stricker
- St Vincent’s Prostate Cancer Research Centre, Sydney, Australia
- Department of Urology, St Vincent’s Private Hospital Sydney, Sydney, Australia
| | - James Thompson
- St Vincent’s Prostate Cancer Research Centre, Sydney, Australia
- Department of Urology, St Vincent’s Private Hospital Sydney, Sydney, Australia
- Department of Urology, St George Hospital, Sydney, Australia
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Coquet J, Bozkurt S, Kan KM, Ferrari MK, Blayney DW, Brooks JD, Hernandez-Boussard T. Comparison of orthogonal NLP methods for clinical phenotyping and assessment of bone scan utilization among prostate cancer patients. J Biomed Inform 2019; 94:103184. [PMID: 31014980 PMCID: PMC6584041 DOI: 10.1016/j.jbi.2019.103184] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Revised: 04/15/2019] [Accepted: 04/19/2019] [Indexed: 01/31/2023]
Abstract
OBJECTIVE Clinical care guidelines recommend that newly diagnosed prostate cancer patients at high risk for metastatic spread receive a bone scan prior to treatment and that low risk patients not receive it. The objective was to develop an automated pipeline to interrogate heterogeneous data to evaluate the use of bone scans using a two different Natural Language Processing (NLP) approaches. MATERIALS AND METHODS Our cohort was divided into risk groups based on Electronic Health Records (EHR). Information on bone scan utilization was identified in both structured data and free text from clinical notes. Our pipeline annotated sentences with a combination of a rule-based method using the ConText algorithm (a generalization of NegEx) and a Convolutional Neural Network (CNN) method using word2vec to produce word embeddings. RESULTS A total of 5500 patients and 369,764 notes were included in the study. A total of 39% of patients were high-risk and 73% of these received a bone scan; of the 18% low risk patients, 10% received one. The accuracy of CNN model outperformed the rule-based model one (F-measure = 0.918 and 0.897 respectively). We demonstrate a combination of both models could maximize precision or recall, based on the study question. CONCLUSION Using structured data, we accurately classified patients' cancer risk group, identified bone scan documentation with two NLP methods, and evaluated guideline adherence. Our pipeline can be used to provide concrete feedback to clinicians and guide treatment decisions.
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Affiliation(s)
- Jean Coquet
- Department of Medicine, Stanford University, Stanford, CA, USA
| | - Selen Bozkurt
- Department of Medicine, Stanford University, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, USA
| | - Kathleen M Kan
- Department of Urology, Stanford University School of Medicine, Stanford, USA
| | - Michelle K Ferrari
- Department of Urology, Stanford University School of Medicine, Stanford, USA
| | - Douglas W Blayney
- Department of Medicine, Stanford University, Stanford, CA, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, USA
| | - James D Brooks
- Department of Urology, Stanford University School of Medicine, Stanford, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, USA
| | - Tina Hernandez-Boussard
- Department of Medicine, Stanford University, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, USA; Department of Surgery, Stanford University School of Medicine, Stanford, USA.
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